Plethysmographic respiration processor

ABSTRACT

A plethysmographic respiration processor is responsive to respiratory effects appearing on a blood volume waveform and the corresponding detected intensity waveform measured with an optical sensor at a blood perfused peripheral tissue site so as to provide a measurement of respiration rate. A preprocessor identifies a windowed pleth corresponding to a physiologically acceptable series of plethysmograph waveform pulses. Multiple processors derive different parameters responsive to particular respiratory effects on the windowed pleth. Decision logic determines a respiration rate based upon at least a portion of these parameters.

PRIORITY CLAIM TO RELATED PROVISIONAL APPLICATIONS

The present application claims priority benefit under 35 U.S.C. §119(e)to U.S. Provisional Patent Application Ser. No. 61/319,256, filed Mar.30, 2010, titled Plethysmographic Respiration Processor and U.S.Provisional Patent Application Ser. 61/364,141, filed Jul. 14, 2010,titled Plethysmographic Respiration Detector; all of the above-citedprovisional patent applications are hereby incorporated by referenceherein.

BACKGROUND OF THE INVENTION

Pulse oximetry is a widely accepted noninvasive procedure for measuringthe oxygen saturation level of arterial blood, an indicator of aperson's oxygen supply. A typical pulse oximetry system utilizes anoptical sensor clipped onto a fingertip to measure the relative volumeof oxygenated hemoglobin in pulsatile arterial blood flowing within thefingertip. Oxygen saturation (SpO₂), pulse rate and a plethysmographwaveform, which is a visualization of pulsatile blood flow over time,are displayed on a monitor accordingly.

Conventional pulse oximetry assumes that arterial blood is the onlypulsatile blood flow in the measurement site. During patient motion,venous blood also moves, which causes errors in conventional pulseoximetry. Advanced pulse oximetry processes the venous blood signal soas to report true arterial oxygen saturation and pulse rate underconditions of patient movement. Advanced pulse oximetry also functionsunder conditions of low perfusion (small signal amplitude), intenseambient light (artificial or sunlight) and electrosurgical instrumentinterference, which are scenarios where conventional pulse oximetrytends to fail.

Advanced pulse oximetry is described in at least U.S. Pat. Nos.6,770,028; 6,658,276; 6,157,850; 6,002,952; 5,769,785 and 5,758,644,which are assigned to Masimo Corporation (“Masimo”) of Irvine, Calif.and are incorporated by reference herein. Corresponding low noiseoptical sensors are disclosed in at least U.S. Pat. Nos. 6,985,764;6,813,511; 6,792,300; 6,256,523; 6,088,607; 5,782,757 and 5,638,818,which are also assigned to Masimo and are also incorporated by referenceherein. Advanced pulse oximetry systems including Masimo SET® low noiseoptical sensors and read through motion pulse oximetry monitors formeasuring SpO₂, pulse rate (PR) and perfusion index (PI) are availablefrom Masimo. Optical sensors include any of Masimo LNOP®, LNCS®,SofTouch™ and Blue™ adhesive or reusable sensors. Pulse oximetrymonitors include any of Masimo Rad-8®, Rad-5®, Rad®-5v or SatShare®monitors.

Advanced blood parameter measurement systems are described in at leastU.S. Pat. No. 7,647,083, filed Mar. 1, 2006, titled Multiple WavelengthSensor Equalization; U.S. patent application Ser. No. 11/367,036, filedMar. 1, 2006, titled Configurable Physiological Measurement System; U.S.patent application Ser. No. 11/367,034, filed Mar. 1, 2006, titledPhysiological Parameter Confidence Measure and U.S. patent applicationSer. No. 11/366,208, filed Mar. 1, 2006, titled NoninvasiveMulti-Parameter Patient Monitor, all assigned to Masimo Laboratories,Irvine, Calif. (Masimo Labs) and all incorporated by reference herein.Advanced blood parameter measurement systems include Masimo Rainbow®SET, which provides measurements in addition to SpO₂, such as totalhemoglobin (SpHb™), oxygen content (SpOC™), methemoglobin (SpMet®),carboxyhemoglobin (SpCO®) and PVI®. Advanced blood parameter sensorsinclude Masimo Rainbow® adhesive, ReSposable™ and reusable sensors.Advanced blood parameter monitors include Masimo Radical-7™, Rad-87™ andRad-57™ monitors, all available from Masimo. Such advanced pulseoximeters, low noise sensors and advanced blood parameter systems havegained rapid acceptance in a wide variety of medical applications,including surgical wards, intensive care and neonatal units, generalwards, home care, physical training, and virtually all types ofmonitoring scenarios.

SUMMARY OF THE INVENTION

Advantageously, a plethysmographic respiration processor providesrespiration rate readings based upon optical properties of pulsatileblood flow. The respiration rate so derived may be used alone orcombined with respiration rate derived by various other means including,but not limited to, microphones or other acoustic sensors located torespond to various body sounds; humidity sensors located to respond toinhalation/exhalation moisture; thermistors and photodiodes located torespond to inhalation/exhalation air temperature; capacitance sensorslocated to respond to inhalation/exhalation air pressure; and venturieffect sensors located to respond to inhalation/exhalation air flow. Ina particularly advantageous embodiment, a plethysmographic respirationdetector is used in conjunction with an acoustic monitor or combinedblood parameter and acoustic monitor, such as a Masimo Rainbow® SETplatform and an acoustic respiration rate (RRa™) sensor available fromMasimo, so as to improve the accuracy of, robustness of, or otherwisesupplement acoustic-derived respiration rate measurements or otheracoustic-derived respiration parameters.

One aspect of a plethysmographic respiration processor is responsive torespiration affecting blood volume and a corresponding detectedintensity waveform measured with an optical sensor at a blood perfusedperipheral tissue site so as to provide a measurement of respirationrate. The plethysmographic respiration detector comprises apreprocessor, processors and decision logic. The preprocessor identifiesa windowed pleth corresponding to a physiologically acceptable series ofplethysmograph waveform pulses. The processors derive various spectrumsof the windowed pleth. Each of the processors is configured so that itscorresponding spectrum is particularly responsive to a specificrespiratory effect on the windowed pleth. The decision logic determinesa respiration rate based upon matching features of at least two of thespectrums.

In various embodiments, the processors comprise a baseline processorthat inputs the windowed pleth and outputs a “baseline” spectrum. Thebaseline processor has a first signal conditioner and a first frequencytransform. The first signal conditioner generates a first conditionedpleth from the windowed pleth. The first frequency transform inputs thefirst conditioned pleth and generates the baseline spectrum.

The processors further comprise an amplitude modulation (AM) processorthat inputs the windowed pleth and outputs an “AM” spectrum. The AMprocessor has a second signal conditioner that generates a secondconditioned pleth from the windowed pleth. A demodulator AM demodulatesthe second conditioned pleth to generate a demodulated pleth. A secondfrequency transform inputs the demodulated pleth and generates the AMspectrum.

The processors further comprise a shape modulation (SM) processor thatinputs the windowed pleth and outputs a “SM” spectrum. The SM processorhas a third signal conditioner that generates a third conditioned plethfrom the windowed pleth. A feature extractor generates a modulatedmetric from the third conditioned pleth. A third frequency transformgenerates the SM spectrum from the modulated metric.

The decision logic has a peak detector, a comparator and a respirationrate output. The peak detector operates on at least two of the baselinespectrum, the AM spectrum and the SM spectrum so as to determine localmaximums. The comparator determines if there are any local maximums fromthe at least two of the spectrums that occur at matching frequencieswithin a predetermined tolerance. A respiration rate output is generatedif the comparator finds at least a two-way match. A smoother operates onmultiple respiration rate outputs derived over a sliding series of thewindowed pleths so as to derive a smoothed respiration rate output. Atested condition rejects the respiration rate output if it differs fromthe smoothed respiration rate output by more than a predeterminedamount.

Another aspect of a respiration rate processor is inputting aplethysmograph waveform, determining a baseline spectrum responsive to arespiratory-induced baseline shift of the plethysmograph waveform,determining an amplitude modulation (AM) spectrum responsive to arespiratory-induced amplitude modulation of the plethysmograph waveform,determining a shape modulation (SM) spectrum responsive to arespiratory-induced shape modulation of the plethysmograph waveform, andmatching at least two of the baseline, AM and SM spectrums so as toderive a respiration rate. In an embodiment, determining a baselinespectrum comprises frequency transforming the plethysmograph waveform.In an embodiment, determining an AM spectrum comprises demodulating theplethysmograph waveform so as to generate a demodulated pleth; andfrequency transforming the demodulated pleth. In an embodiment,determining a SM spectrum comprises feature extracting theplethysmograph waveform so as to generate a modulated metric andfrequency transforming the modulated metric.

In various other embodiments, matching comprises detecting peaks in atleast two of the spectrums, comparing the detected peaks so as to findone peak from each of the at least two spectrums occurring at aparticular frequency and outputting the particular frequency as therespiration rate. Windowed pleths are defined by a sliding window ofacceptable portions of the plethysmograph waveform. The respiration rateoutput is smoothed based upon a median respiration rate calculated overmultiple ones of the windowed pleths. The particular frequency isrejected if it is not within a predetermined difference of the smoothedrespiration rate.

A further aspect of a respiration rate processor is a baselineprocessor, an AM processor, a SM processor and decision logic. Thebaseline processor identifies a respiration-induced baseline shift in aplethysmograph waveform. The AM processor identifies arespiration-induced amplitude modulation of the plethysmograph waveform.The SM processor identifies a respiration-induced shape modulation ofthe plethysmograph waveform. The decision logic compares therespiration-induced baseline shift, amplitude modulation and shapemodulation so as to derive a respiration rate.

In various embodiments, the baseline processor generates a baselinespectrum from a first frequency transform of the plethysmographwaveform. The AM processor generates an AM spectrum from a secondfrequency transform of demodulated plethysmograph waveform. The SMprocessor generates an SM spectrum from a third frequency transform of amodulated metric extracted from the plethysmograph waveform. Decisionlogic has a peak detector and a comparator. The peak detector determineslocal maximums in each of the baseline spectrum, AM spectrum and SMspectrum. In an embodiment, the comparator determines a three-way matchin the frequency of the local maximums in the spectrums. In anembodiment, the comparator determines a two-way match in the frequencyof the local maximums in the spectrums, and a condition for acceptingthe two-way match compares a respiration rate determined by the two-waymatch to a smoothed respiration rate.

A further aspect of a plethysmographic respiration processor isresponsive to respiratory modulation of a blood volume waveform orcorresponding detected intensity waveform measured with an opticalsensor at a blood perfused peripheral tissue site so as to provide ameasurement of a respiration parameter. A demodulator processes a sensorsignal so as to generate a plethysmograph waveform. A pulse processoridentifies candidate pulses from the plethysmograph waveform. A pulsemodeler identifies physiologically acceptable ones of the candidatepulses. The plethysmographic respiration processor has a featureextractor, a normalizer and a feature analyzer. The feature extractorprocesses the acceptable pulses so as to calculate pulse features. Thenormalizer compares the pulse features so as to calculate a pulseparameter. The feature analyzer calculates a respiration parameter fromthe pulse parameter.

In various embodiments, the pulse features comprise a difference (E)between an acceptable pulse and a triangular pulse estimate; the pulsefeatures comprise an area (A) under a triangular pulse; or the pulsefeatures are calculated with respect to a diastolic (d) portion of anacceptable pulse and a corresponding diastolic portion of a triangularpulse. In various embodiments, the normalizer compares a diastolicdifference (Ed) with a diastolic area (Ad) or the normalizer calculatesEd/Ad. In an embodiment, the feature analyzer determines the frequencyspectrum of Ed/Ad so as to determine a respiration rate.

Yet another aspect of a plethysmographic respiration processor detects atissue site response to optical radiation having a plurality ofwavelengths, demodulates the response according to wavelength so as togenerate a corresponding plurality of plethysmograph waveforms,identifies acceptable pulses from at least one of the waveforms andcalculates a respiration parameter from the acceptable pulses. Tocalculate a respiration parameter, in various embodiments the processorestimates an acceptable pulse with a triangular pulse and determines asystolic portion and a diastolic portion of the acceptable pulse and thetriangular pulse; compares the triangular to the acceptable pulse so asto define pulse features; normalizes the pulse features according to thesystolic and diastolic portions so as to generate a pulse parameter; oranalyzes the pulse parameter to derive a respiration parameter. Thecomparing may comprise differencing the acceptable pulse and thetriangular pulse over the diastolic portion. The analyzing may comprisetransforming the pulse parameter to a frequency parameter and outputtinga respiration rate according to a maximum of the frequency parameter.

Additional aspects of plethysmographic respiration processor has a pulseinput having physiologically acceptable pleth pulses derived from aplethysmograph waveform. A feature extractor extracts pulse featuresfrom the pulse input. The pulse features are modulated by respiration. Anormalizer calculates a pulse parameter from the relative magnitude of afirst one of the pulse features compared with a second one of the pulsefeatures. A feature analyzer calculates a respiration parameter from thepulse parameter.

In various embodiments, the feature extractor may calculate a differencebetween a triangular pulse estimate and a corresponding pleth pulse. Thefeature may also calculate an area under a portion of the triangularpulses. The processor may differentiate between a systolic pulse featureand a diastolic pulse feature. The feature extractor may calculate anapex angle of the slope portion of a triangular pulse estimate. Thefeature analyzer may perform a frequency transform to extract arespiration rate from the pulse parameter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a plethysmographic respiration processorembodiment;

FIGS. 2A-B are a block diagram of a pre-processor embodiment and a timeillustration of a sliding window, respectively;

FIGS. 3A-C are a block diagram of a baseline processor, an intensityversus time graph of a baseline modulated pleth, and a baselinefrequency spectrum, respectively;

FIGS. 4A-D are a block diagram of an AM processor, an intensity versustime graph of an AM pleth, and AM pleth frequency spectrum and ademodulated pleth frequency spectrum, respectively;

FIGS. 5A-D are a block diagram of an SM processor, an intensity versustime graph of an SM pleth, and graph of a shape metric versus time; anda shape metric frequency spectrum, respectively;

FIGS. 6A-B are intensity versus time graphs of a shape modulated pulseillustrating area-based shape metrics;

FIGS. 7A-B are intensity versus time graphs of a shape modulated pulseillustrating arc-length shape metrics;

FIG. 8A-D are a block diagram of an FM processor, an intensity versustime graph of an FM pleth, and graph of a dicrotic-notch based FM metricversus time; and a FM metric frequency spectrum, respectively;

FIG. 9 is a block diagram of a pre-processor embodiment;

FIG. 10 is a block diagram of a plethysmographic respiration processorembodiment;

FIGS. 11A-D are a spectrums of a combined baseline shifted and AMmodulated pleth; a spectrum of a demodulated baseline shifted and AMmodulated pleth; a SM spectrum; and a non-idealized spectrum,respectively;

FIG. 12 is a decision logic flowchart for advantageously deriving arobust value for respiration rate based upon a baseline, an AM and a SMprocessor operating on an acceptable window of pleths; and

FIG. 13 is a decision logic flowchart for advantageously deriving arobust value for respiration rate based upon a baseline, an AM and ahigh pass filtered (HPF) SM processor operating on an acceptable windowof pleths.

FIG. 14 is a perspective view of a non-invasive physiological parametermeasurement system having a monitor and a corresponding optical sensorand incorporating a plethysmographic respiration processor;

FIGS. 15A-B are graphs of light absorption profiles for pulsatile bloodperfused tissue and surrounding tissue and an optical sensor detectedlight intensity, respectively;

FIG. 16 is block diagram of a non-invasive physiological parametermeasurement system having a monitor and a corresponding optical sensorand incorporating a plethysmographic respiration processor; and

FIG. 17 is a block diagram of a modulated plethysmograph demodulator.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 illustrates a plethysmographic respiration processor 100embodiment having a plethysmograph waveform (pleth) input 101 and arespiration rate (RR) output 104. The pleth respiration processor 100includes a preprocessor 200, one or more pleth processors 120 and a postprocessor 130. The pleth 101 is derived from an optical sensor attachedto a tissue site, which is in communications with a pulse oximeter orblood parameter monitor, as described with respect to FIGS. 14-17,below. The pre-processor 200 derives acceptable pleths 112, as describedin detail with respect to FIGS. 2A-B, below. The pleth processor(s) 120each operate on acceptable pleths 112 so as to generaterespiration-rated parameters 102 responsive to a person's respiration.The pleth processors 120 may operate in the time domain, the frequencydomain or a mix of time or frequency domains. Pleth processors 120 aredescribed in detail with respect to FIGS. 3-8, below. The post-processor130 resolves or otherwise verifies the respiration-related parameters102 so as to derive a respiration rate and, perhaps, averages, smoothesor otherwise filters that respiration rate so as to generate therespiration rate (RR) 104 output. Advantageously, this optical sensorderived RR may be used to derive a less intrusive measure of respirationrate or used in combination with acoustic, mechanical, electrical,temperature or other sensors and monitors so as to determine a moreaccurate or robust measure of respiration rate. Although describedherein as deriving a respiration rate, a plethysmographic respirationprocessor 100 output may be similarly expressed as a respiration or abreathing frequency or interval, among others.

FIGS. 2A-B illustrate a pre-processor 200 having a pleth 101 input andgenerating a conditioned pleth 112, as described below. Thepre-processor 200 has a candidate pulse processor 220, a pulse modeler230, a sliding window 240, a pleth windower 250 and a signal conditioner260. A single pleth channel is selected from a multiple demodulatedpleths 1705 (FIG. 17) as a representative pleth 101 input. In anembodiment, the representative pleth channel corresponds to the IRwavelength channel of a (two wavelength) pulse oximeter.

As shown in FIG. 2A, the pleth 101 is fed into a candidate pulseprocessor 220 that removes noise and artifacts and identifies the startand end of pulses that conform to various tests of physiologicalacceptability. In an embodiment, the candidate pulse processor 220 hascurvature, low-pass filter and edge finder components that removewaveform features that do not correspond to the steep inflow phaseduring ventricular systole or the longer outflow phase during diastole,including the characteristic dichrotic notch and miscellaneous waveformcurvature changes. Accordingly, the candidate pulse processor 220identifies “edges” within an input waveform segment that connect a peakand subsequent valley of a pleth pulse. The candidate pulse processor220 also has delta T, zero crossing, amplitude threshold and slopechecks so as to eliminate certain of the edges that were identified bythe curvature, filter and edge finder components that do not meetcertain conditions. The delta T discards all the edges that are eithertoo slow or too quick to be physiological. The zero crossing checkeliminates all edges that do not cross the zero line, such as smallbumps that are not peaks or valleys. The amplitude threshold checkremoves larger “bumps” than the zero crossing check, such as dicroticnotches. The slope check is based on the observation that in aphysiological pulse, the ventricular contraction, i.e. descending pulseportion, is steeper than any subsequent trend in the ascending pulseportion. The pulse finder transforms the edges remaining after thevarious edge checks into candidate pulses 222, which are fed into thepulse modeler 230.

Also shown in FIG. 2A, the pulse modeler 230 takes the candidate pulses222 and identifies which of these are acceptable pulses 232, whichsatisfy an internal model for a physiological plethysmographic waveform.Although the candidate pulse processor 220 performs a series of checkson edges, the pulse modeler 230 performs a series of checks on pulsefeatures. The first component of the pulse modeler calculates relevantpulse features. The remainder of the pulse modeler checks these pulsefeatures to identify physiologically acceptable features. The pulsefeatures component extracts three items of information about the inputcandidate pulses that are needed for downstream processing by the othercomponents of the pulse modeler including pulse starting point, periodand signal strength. The downstream components include a max BPM check,a stick model check, an angle check, a ratio check and a signal strengthcheck. The maximum beats-per-minute (max BPM) check discards pulseshaving a period that is below a minimum number of samples. The stickmodel check discards pulses where the corresponding waveform does notfit a stick model. The angle check is based on computing the angle of anormalized slope for the ascending portion of a pulse so as to discardpulses that are extremely asymmetric. The ratio check removes pulses inwhich the ratio between the duration of the ascending pulse portion andthe duration of the descending pulse portion is less than a certainthreshold. The signal strength check assigns a confidence value to eachpulse, based on its signal strength, and low confidence pulses arediscarded. A pulse processor 220 and a pulse modeler 230 are describedin U.S. Pat. No. 6,463,311 titled Plethysmograph Pulse RecognitionProcessor, issued Oct. 8, 2002, assigned to Masimo Corporation andincorporated by reference herein.

Further shown in FIGS. 2A-B, the sliding window 240 defines a series offixed-time-length (T) samples (“windows”) of pleth, where each window242 is shifted from the previous window by a fixed time interval (Δt)244. In an embodiment, each window is T=2125 samples (34 sec) in lengthat 16 msec per sample (62.5 Hz sample rate), where successive windowsare shifted by Δt=2 sec. Each window is either accepted 242 or rejected248 as designated by an acceptable window 242 output. The pleth windower250 utilizes the acceptable windows 242 designation to accept acorresponding section of the “raw” pleth 101 input and generate awindowed (raw) pleth 252 output. That is, the pre-processor 200advantageously allows downstream processing to operate directly on thedemodulated pleth while discarding those raw pleth sections that aredeemed unacceptable, based upon various pleth models and checks asdescribed above. In an embodiment, the signal conditioner 260demeans/detrends and bandpass filters the windowed pleth 252 to generatea conditioned pleth 112 output. In an embodiment, the bandpass filter isan IIR filter having a 12-240 bpm (beats per minute) passband. Inanother embodiment described below with respect to FIG. 9, below, apre-processor 200 generates windowed features from acceptable pulsesderived from the pleth 101 input.

FIGS. 3A-B illustrate a baseline processor 300 that derives a “baseline”spectrum F_(b) 302 responsive to a respiration-induced baseline shift ina pleth. As shown in FIG. 3A, the baseline processor 300 has aconditioned pleth 301 input and generates a corresponding baselinespectrum F_(b) 302. As shown in FIG. 3B, the conditioned pleth 301 has apleth period 381 inversely related to pulse rate (PR). Under certainconditions, an individual's respiration induces a cyclical shift in thepleth baseline 382. The cyclical shift period 383 is inversely relatedto respiration rate (RR). As shown in FIG. 3C, a frequency spectrum 302of the baseline-shifted pleth 301 includes a relatively large pulse rate(PR) peak 392 and a relatively small respiration rate (RR) peak 391.

In other embodiments, a baseline processor 300 employs a time domaincalculation of the conditioned pleth 301 that determines the period 383of a cyclical baseline shift and hence respiration rate. Such a timedomain calculation may be based upon envelope detection of theconditioned pleths 301, such as a curve-fit to the peaks (or valleys) ofthe pleth pulses. Measurements of a cyclical variation in aplethysmograph baseline are described in U.S. patent application Ser.No. 11/221,411 titled Noninvasive Hypovolemia Monitor, filed Sep. 6,2005 and published as US 2006/0058691 A1, assigned to Masimo andincorporated by reference herein.

FIGS. 4A-B illustrate an AM processor 400 that derives an “AM” spectrumF_(am) 402 responsive to a respiration-induced amplitude modulation ofthe pleth. As shown in FIG. 4A, the AM processor 400 has a conditionedpleth 401 input and generates a corresponding AM spectrum F_(am) 402that is responsive to a demodulated pleth 422. In an embodiment, thedemodulator 420 squares, low pass filters (LPF) and square-roots theconditioned pleth 401 to generate a demodulated pleth 422. In anembodiment, the frequency transformation 430 utilizes a Hamming window,a chirp-Z FFT algorithm and a magnitude calculation so as to generate anAM spectrum 402 for the demodulated pleth 422.

As shown in FIG. 4B, a pleth 401 has a pleth period 471 inverselyrelated to pulse rate (PR). Under certain conditions, an individual'srespiration amplitude modulates (AM) 472 the plethysmograph 401. Inparticular, the modulation period 473 is inversely related torespiration rate (RR). As shown in FIG. 4C, a spectrum 480 of the pleth401 includes a pulse rate (PR) peak 481 and respiration sidebands 482,483 displaced by RR on either side of the PR peak 481. As shown in FIG.4D a spectrum 402 of the demodulated pleth 422 includes a DC peak 491resulting from the demodulated pulse rate “carrier” translated to DC anda respiration rate (RR) peak 492 resulting from the demodulatedsidebands 482, 483.

An AM processor 400 is described above as demodulating 420 a conditionedpleth 401. In other embodiments, a time domain calculation of theconditioned pleth 401 determines the respiration modulation period 473and hence the respiration rate. That time domain calculation may bebased upon envelope detection of the conditioned pleth 401, such as acurve-fit to the peaks (or valleys) of the plethysmograph or,alternatively, the peak-to-peak variation. Measurements of variation ina plethysmograph envelope are described in U.S. patent application Ser.No. 11/952,940 titled Plethysmograph Variability Processor, filed Dec.7, 2007 and published as US 2008/0188760 A1, assigned to Masimo andincorporated by reference herein.

FIGS. 5A-D illustrate a shape modulation (SM) processor 500 that derivesan “SM” spectrum F_(s) 502 responsive to a respiration-induced shapemodulation of the pleth. As shown in FIG. 5A, the SM processor 500 has aconditioned pleth 501 input and generates a corresponding SM spectrum Fs502. In an embodiment, the SM processor 500 includes a feature extractor520, a high pass filter (HPF) 530 and a frequency transform 540. Inanother embodiment, the SM processor includes the feature extractor 520and a frequency transform 540, but excludes the high pass filter 530.The feature extractor 520 generates a shape-based modulated metric 522,such as E/A described below. In an embodiment, the HPF 530 is atime-domain difference filter that calculates y_(n+1)−y_(n) so as toremove an erroneous first (low frequency) peak in the SM spectrum Fs502. In an embodiment, the frequency transformation 540 utilizes aHamming window, a chirp-Z FFT algorithm and a magnitude calculation soas to generate the SM spectrum 502 for each windowed conditioned pleth501.

As shown in FIG. 5B, a pleth 501 has a pleth period 571 inverselyrelated to pulse rate (PR). Under some circumstances, an individual'srespiration modulates the shape of each pleth pulse. This modulation maybe described in terms of a predefined pleth feature or “shape metric.”In an advantageous embodiment, a shape metric is defined by a differenceor “error” E 572 between the diastolic portion of a pleth pulse and itscorresponding triangular pulse approximation, normalized by the area A573 under the triangular pulse approximation.

As shown in FIG. 5C, a respiration-modulated shape metric 522 has acyclical period 581 inversely related to respiration rate (RR). As shownin FIG. 5D, a spectrum 502 of the modulated shape metric 522 includes arespiration rate (RR) peak 591.

A SM processor 500 is described above as based upon a normalizeddiastolic error metric (E/A). In other embodiments, shape metrics may bebased upon other pulse features such as a diastolic area, error or anglenormalized by the corresponding systolic area, error or angle (Ad/As,Ed/Es, θd/θs), or shape metrics may be related to the arc length of thediastolic and/or systolic portions of a pleth pulse, to name a few.These and other pulse shapes and features responsive to respiration arealso contemplated herein.

FIGS. 6A-B further illustrates pulse shape features that are derived bya feature extractor 520 (FIG. 5A) embodiment. Acceptable pleth pulses610 are generated by the pre-processor 200 (FIG. 2A), as describedabove. For convenience of illustration, the inverse of an “intensity”pulse is shown, as described with respect to FIGS. 15A-B, below. Thepleth pulse 610 has a peak Y at a time W and corresponding valleys attimes X and Z. The peak and valleys define a triangular pulse 620 XYZthat approximates the pleth pulse 610. Further, the time line WYcorresponding to the peak Y divides the pleth pulse 610 into a systolicportion 630 and a diastolic portion 640.

As shown in FIG. 6A, a systolic error Es 631 is defined as the totalarea between the pleth pulse 610 and the approximate triangular pulse620 within the systolic portion 630. A diastolic error Ed 641 is definedas the total area between the pleth pulse 610 and the triangular pulse620 within the diastolic portion 640.

As shown in FIG. 6B, a systolic area As 632 is defined as the total areaunder the triangular pulse 620 within the systolic portion 630. Adiastolic area Ad 642 is defined as the total area under the triangularpulse 620 within the diastolic portion 640. A systolic angle θs 633 isdefined as the angle XYW defined by the triangular pulse within thesystolic portion 630. A diastolic angle θd 643 is defined as the angleZYW defined by the triangular pulse within the diastolic portion 640.

Based upon the above-described pulse feature definitions, normalizedpulse features may be defined. These may include normalized diastolicpulse features, such as Ed/Ad, corresponding to the diastolic triangularpulse error normalized by the diastolic triangular pulse area. Othernormalized diastolic pulse features may include a diastolic area, erroror angle normalized by the corresponding systolic area, error or angle(Ad/As, Ed/Es, θd/θs).

FIGS. 7A-B illustrate additional pulse shape features. As shown in FIG.7A, pulse features may be based upon the length of a curve (trace, arc,path or line) portion of a pleth pulse 710. In particular, a diastoliccurve length Ld 745 between the pulse peak Y and valley Z is defined inpolar coordinates as:

$\begin{matrix}{{L\; d} = {\int_{Y}^{Z}\sqrt{r^{2} + {\left( \frac{\mathbb{d}r}{\mathbb{d}\theta} \right)^{2}{\mathbb{d}\theta}}}}} & \left( {{EQ}.\mspace{14mu} 1} \right)\end{matrix}$where r is the distance from W (time corresponding to the peak Y) to anypoint V along the curve 710 and θ is the angle between r and the timeaxis WZ. Ld 745 may be similarly defined in Cartesian coordinates. Asystolic curve length Ls 735 may be defined in similar fashion. Anormalized length pulse feature Ld/Ls may be defined accordingly. Inother embodiments, pulse features Ld 745 or Ls 735 may be normalized bythe diastolic 640 or systolic 630 areas or angles defined with respectto FIG. 6B, above. In various embodiments, pulse features Ld 745 or Ls735 also may be normalized by pulse height WY, by diastolic WZ orsystolic XW pulse widths, by total pulse width XZ or by mathematicalcombinations of these measures of pulse height and pulse width to name afew.

As shown in FIG. 7B, pulse shape features may be based upon thecurvature of a portion of a pleth pulse 710. In particular, a curvatureκ is defined in pedal coordinates as:

$\begin{matrix}{\kappa = {\frac{1}{r}\frac{\mathbb{d}p}{\mathbb{d}r}}} & \left( {{EQ}.\mspace{14mu} 2} \right)\end{matrix}$where the pedal coordinates of a point V with respect to the pulse 710and the pedal point W are the radial distance r from W to V and theperpendicular distance p from W to the line t tangent to the pulse 710at V, as shown. κ may be similarly defined in Cartesian or polarcoordinates. Total curvature K of a curve segment between points a and bis then

$\begin{matrix}{K = {\int_{a}^{b}{{\kappa(s)}{\mathbb{d}s}}}} & \left( {{EQ}.\mspace{14mu} 3} \right)\end{matrix}$A diastolic curvature Kd 746 or systolic curvature Ks 736 pulse shapefeature may be defined accordingly. In other embodiments, a curvaturepulse shape feature may be defined according to the absolute value ofthe maximum and/or minimum curvature of the pulse 710 or pulse segment730, 740, or the curvature of a particular feature, such as a dicroticnotch. In other embodiments, pulse shape features Kd 746 or Ks 736 maybe normalized by the diastolic 640 or systolic 630 areas or anglesdefined above with respect to FIGS. 6A-B. In various embodiments, pulsefeatures Kd 746 or Ks 736 also may be normalized by pulse height WY, bydiastolic WZ or systolic XW pulse widths, by total pulse width XZ or bymathematical combinations of these measures of pulse height and pulsewidth to name a few. In other embodiments, various normalized systolicand/or diastolic pulse features may be similarly defined.

FIG. 8A-D illustrate an FM processor 800 that derives an “FM” spectrumF_(fm) 802 responsive to a respiration-induced frequency modulation ofthe pleth. As shown in FIG. 8A, the FM processor 800 has a conditionedpleth 112 input and generates a corresponding FM spectrum F_(fm) 802that is responsive to a demodulated pleth 822. In an embodiment, thedemodulator 820 utilizes a metric Δ responsive to the time differencebetween identifiable epochs of each pleth pulse. In an embodiment, theepochs are based upon a dicrotic notch. In an embodiment, the metric Δis the time difference between two identifiable portions of a dicroticnotch such as the notch local maximum, local minimum, or mid-pointbetween local maximum and local minimum, to name a few.

-   -   As shown in FIG. 8B, a pleth 801 has a pleth period 871        inversely related to pulse rate (PR). Under certain conditions,        an individual's respiration frequency modulates (FM) the        plethysmograph 801. In particular, the modulation period 881        (FIG. 8C) is inversely related to respiration rate (RR). FIG. 8C        illustrates a respiration-modulated FM metric 822 over time. In        particular, an FM metric 822, such as the metric Δ described        above, has a cyclical period 881 inversely related to        respiration rate (RR). As shown in FIG. 8D, a spectrum 802 of        the FM metric 822 includes a respiration rate (RR) peak 891.

FIG. 9 illustrates another pre-processor 900 embodiment having a pleth101 input and generating a windowed features 962 output. Thepre-processor 900 has a candidate pulse processor 220 and pulse modeler230 that operate on the pleth 101 input so as to generate an acceptablepulses 232 output, as described with respect to FIG. 2A, above. Further,the pre-processor 900 has a time base standard 940, a featureextractor/normalizer 950, and a windowing/outlier rejecter 960. The timebase standard 940 inputs acceptable pulses 941 and outputs resizedpulses 942. In particular, the time base standard 940 mathematicallyre-samples the input pulses 941 so that each pulse has the same numberof samples. For example, if a standard pulse has 50 samples and an inputpulse 941 has 60 samples, then the input pulse 941 sample interval ismade larger by 60/50 or 1.2 times so that the resized input pulse widthis 50 samples. Similarly, if an input pulse 941 has 40 samples, then theinput pulse 941 sample interval is made smaller by 40/50 or 0.8 times sothat the resized input pulse width is 50 samples. A resized input pulseis derived by interpolating the original pulse at re-sampled points. Forexample, a linear interpolation embodiment is used according to thefollowing

$\begin{matrix}{y = {{\left( \frac{y_{2} - y_{1}}{x_{2} - x_{1}} \right)\left( {x - x_{1}} \right)} + y_{1}}} & \left( {{EQ}.\mspace{14mu} 4} \right)\end{matrix}$where x₂−x₁ is the original sample interval; y₁ and y₂ are input pulse401 values at x₁ and x₂, respectively; x is a resized sample pointbetween x₁ and x₂ and y is the resized pulse value at x. In otherembodiments, the interpolation is a cubic spline or a polynomialinterpolation to name a few.

Also shown in FIG. 9, the feature extractor/normalizer 950 inputs theresized pulses 942 described above and outputs normalized pulse features952. Pulse features may include one or more of the differences or“errors” E between an acceptable pulse and its corresponding triangularpulse; areas A under the triangular pulse; and apex angles θ of atriangular pulse, to name a few, as described in detail with respect toFIGS. 6-7, above. Pulse features may also distinguish between asteeper-slope portion corresponding to systole S and a shallower-slopeportion corresponding to diastole D. Pulse features are normalized bycomparing one or more extracted features with one or more otherextracted features. In an embodiment, normalized pulse features 952advantageously include Ed/Ad corresponding to the diastolic triangularpulse error normalized by the diastolic triangular pulse area. Othernormalized pulse features 952 may include a diastolic area, error orangle normalized by the corresponding systolic area, error or angle(Ad/As, Ed/Es, θd/θs). These and additional normalized pulse featuresrelating to an acceptable pulse and/or its corresponding triangularpulse are also contemplated herein and described with respect to FIGS.6-7, above.

Further shown in FIG. 9, the windowing/outlier rejecter 960 inputs thenormalized features 952 and outputs windowed features 962. The windowedfeatures 962, in turn, may be frequency transformed or analyzed in thetime domain to determine a respiration modulation of the features, asdescribed above. In particular, windowing 960 defines a sample size(window size) of the normalized features 952. The outlier rejector 960calculates a mean or median of the normalized features 952 fallingwithin the window, defines an acceptable range around the mean or medianand rejects normalized features falling outside of that acceptablerange.

Window size may be a function of a respiration rate (RR) 964, a heartrate (HR) 966 or both. In particular, HR 966 corresponds to the inputpulse 101 frequency and hence determines the time between samples of thenormalized features 952. RR 964 corresponds to the number of featurecycles within a window and hence sets a lower limit on the window sizein order to resolve the frequency of those feature cycles.

Pulse rates may typically vary from a resting rate of 40-60 BPM forathletes to 60-90 BPM for non-athletes. Maximum heart rates aretypically defined as 220—age. Hence, pulse rates might typically rangefrom 50 to 200 BPM, which is a 4:1 variation in time between samples(0.3 sec to 1.2 sec). Respiration rates may typically vary between 12-20breaths per minute for resting adults to 35-45 breaths per minute forexercising adults. Hence RR may typically range from 10-50 breaths perminute, which is a 5:1 variation in the number of respiration cycles perwindow. Accordingly, the number of pulse feature samples per respirationcycle may have a 20:1 variation.

Windowing 960 may be fixed or adjustable. Further, successive windowsmay be overlapping, i.e. a sliding window may be used, or may beadjacent and non-overlapping. A typical window size may range, say,between 15-120 sec. or more. Accordingly, a window size may encompass,say, 20 respiration cycles at 10 breaths per minute over a 120 sec.window to 12 respiration cycles at 50 breaths per minute over a 15 sec.window. In an embodiment, the window size is adaptively adjusted basedupon detected RR and PR.

FIG. 10 illustrates a plethysmographic respiration processor 1000embodiment having a conditioned plethysmograph waveform (pleth) 112input and a smoothed respiration rate (RRs) 1005 output. The respirationprocessor 1000 includes parallel processors 1020, decision logic1100-1300 and a smoother 1030. The conditioned pleth 112 contains plethsections corresponding to sliding acceptable windows designated by thepre-processor 200 (FIG. 2A), as described above with respect to FIGS.2A-B. The parallel processors 1020 each operate on conditioned pleth 112so as to generate frequency spectrums 1022 responsive to respirationrate. The parallel processors 1020 include a baseline processor 300, anamplitude modulation (AM) processor 400, a high pass filtered (HPF)shape modulation (SM) processor 500 and a SM processor 501. Inparticular, the baseline processor 300 derives a “baseline” spectrumF_(b) 302 responsive to a respiration-induced baseline shift in a pleth.The baseline processor 300 is described in detail with respect to FIGS.3A-C, above. The AM processor 400 derives an “AM” spectrum F_(am) 402responsive to a respiration-induced amplitude modulation of the pleth.The AM processor 400 is described in detail with respect to FIGS. 4A-D,above. The SM processors 500, 501 derive “SM” spectrums F_(s) 502,F_(s)′ 504 each responsive to a respiration-induced shape modulation ofthe pleth. The SM processors 500, 501 are described in detail withrespect to FIGS. 5-7, above.

As described above, the processors 1020 each generate one spectrum 1022for each sliding window of the conditioned pleth 112. Accordingly, thedecision logic 1100-1300 attempts to generate a respiration rate (RR)value for each conditioned pleth 112 window. The decision logic1100-1300 compares two or more of the spectrums F_(b), F_(am), F_(s) andF_(s)′ 422 so as to calculate a respiration rate (RR) 1004. If thedecision logic 1100-1300 cannot determine a RR 1004 value from thespectrums 1022, the corresponding conditioned pleth window 112, isrejected. A smoother 1030 generates a smoothed respiration rate 1005calculated over multiple respiration rate 1004 values. In an embodiment,the smoother 1030 determines the median value of RR 1004 correspondingto multiple ones of the conditioned pleth windows 112. In an embodiment,the median value is calculated over five conditioned pleth windows 112.The decision logic 1100-1300 is described in detail with respect toFIGS. 11-13, below.

FIGS. 11A-C illustrate the output of the baseline processor 300 (FIG.10), AM processor 400 (FIG. 10) and SM processor 500 (FIG. 10),respectively, assuming that a conditioned pleth 112 (FIG. 10) exhibitseach of a baseline shift, an amplitude modulation and a shape modulationdue to respiration. As shown in FIG. 11A, in view of both arespiration-induced baseline shift and AM modulation, the windowed plethspectrum 1110 is a combination of a baseline shift spectrum 302 (FIG.3C) and an AM spectrum 402 (FIG. 4D). This combination is also thebaseline spectrum F_(b) 302 (FIG. 3A), i.e. the frequency transform ofthe conditioned pleth 112. Hence, in this example, the baseline spectrum1110 has two possible local maximums or “peaks” 1115, 1116. One peak isdue to respiration shifting the pleth baseline and one peak is due torespiration amplitude modulating the pleth. However, these peaks cannotbe distinguished. In particular, if RR<0.5PR, then peak 1115 is at afrequency corresponding to RR and peak 1116 is at a frequencycorresponding to PR-RR. Likewise, if RR is >0.5 PR, then peak 1115 is ata frequency corresponding to PR-RR and peak 1116 is at a frequencycorresponding to RR. That is, “twin” peaks 1115, 1116 occursymmetrically on either side of frequency ½ PR, one at frequency RR andone at frequency PR-RR, but the peak corresponding to the respirationrate RR cannot be resolved by the baseline processor 302 (FIG. 10)alone.

As shown in FIG. 11B, in view of both a respiration-induced baselineshift and AM modulation, the AM spectrum F_(am) 402 (FIG. 10) is acombination of the spectrums of FIG. 3C and FIG. 4C after demodulation.Hence, in this example, the AM processor output 402 (FIG. 10) has twopossible local maximums or peaks 1125, 1126. One peak is due todemodulating the pleth corresponding to the spectrum of FIG. 4C,resulting in the spectrum of FIG. 4D. The other peak is due todemodulating the pleth corresponding to the spectrum of FIG. 3C, whichtranslates the pleth fundamental 392 (FIG. 3C) at PR to DC and therespiration-related peak 391 (FIG. 3C) to PR-RR. As with the peaksdescribed with respect to FIG. 11A, these “twin” peaks 1125, 1126 occursymmetrically on either side of ½PR, but the peak corresponding to therespiration rate RR cannot be resolved by the AM processor 400 (FIG. 10)alone.

As shown in FIG. 11C, the SM spectrum F_(s) 502 is unaffected by eithera baseline shift or by amplitude modulation. In particular, arespiration-induced baseline shift, which shifts the entire plethwaveform up or down, has negligible effect on the error E 572 (FIG. 5B)or the triangular area A 573 (FIG. 5B). Further, althoughrespiration-induced AM increases or decreases the pleth amplitude, thisis accounted for by normalizing the error E by the triangular area A. Assuch, in view of both a respiration-induced baseline shift and AM, theSM spectrum F_(s) 502 is responsive only to shape modulation, as shownin FIG. 5D, i.e. a single local maximum or peak 1135 occurs at therespiration rate.

As shown in FIGS. 11A-C, ideally respiration rate may be determined byfirst verifying the existence of twin peaks 1115, 1116 symmetric about0.5PR in the baseline spectrum 1110 and twin peaks about 0.5PR in the AMspectrum 1120. Second, one twin from each spectrum 1110, 1120 is matchedwith the single peak in the SM spectrum 1130. For example, a matchbetween peaks 1115 (FIG. 11A), 1125 (FIG. 11B) and 1135 (FIG. 11C) wouldprovide a robust indication of RR. However, pleths from various sensors,monitors and patients may yield spectrums with erroneous peaks due tophysiological conditions or artifact. Accordingly, various peaks andmatching conditions are utilized by the decision logic to determine RR,as described with respect to FIGS. 12-13, below.

As shown in FIG. 11D, a peak identifying nomenclature 1140 is used indescribing decision logic with respect to the baseline spectrum F_(b)302 (FIG. 10) and the AM spectrum F_(am) 402 (FIG. 10). The largest peakin a spectrum is designated {circle around (1)} and its twin designated{circle around (4)}. If the largest peak is the first peak, which issometimes erroneous, then the second largest peak is designated {circlearound (2)} and its twin designated {circle around (5)}. If the largestpeak is the last peak, which is also sometimes erroneous, the secondlargest peak is designated {circle around (3)} and its twin designated{circle around (6)}.

FIG. 12 illustrates the decision logic 1200 for advantageously derivinga robust value for respiration rate based upon each of the baseline 300,AM 400 and SM 500 processors (FIG. 10) operating on a conditioned pleth112 (FIG. 10). The spectrums F_(b), F_(am) and F_(s) from theseprocessors are input 1210 into the decision logic 1200. A peak detector1220 locates the largest peak {circle around (1)} and its twin {circlearound (4)} from each of F_(b) 1110 (FIG. 11A) and F_(am) 1120 (FIG.11B) and the largest peak {circle around (1)} from F_(s) 1130 (FIG.11C). The comparator 1230 looks for a three-way match from, say, thelargest peak from each of the spectrums. This comparison is denoted1-1-1, designating the largest peaks from the spectrumsF_(b)-F_(am)-F_(s), respectively. If the frequencies of all of thesepeaks match 1240, within a predetermined error, then that frequency isoutput as the respiration rate 1250 for that conditioned pleth window112 (FIG. 10). If there is no match 1240, other combinations 1260, 1266of peaks of a particular series are compared 1230, such as the largestpeak from F_(b), the twin to the largest peak from F_(am) and thelargest peak from F_(s), denoted 1-4-1. Hence, all of the followingcombinations are denoted the first series of combinations to try, i.e.series I: 1-1-1; 1-4-1; 4-1-1; 4-4-1.

As shown in FIG. 12, if there are no matches from series I, other series1270, 1275 having different types of combinations are tried, asexplained below. If a particular twin cannot be located, thecorresponding series is rejected 1275. If no 3-way matching peaks arefound after trying all combinations in each of series I, II, III, IV1270, then that particular window is rejected 1280 and no respirationrate value is determined that corresponds to that window.

Series II represents a second set of peak comparisons. In some cases,the largest peak {circle around (1)} from F_(b) or F_(am) or both may bethe first peak, which is often erroneous. As such, comparisons may bemade using the second largest peaks {circle around (2)} from Fb and Fmand the corresponding twins {circle around (5)}. The twins in thisseries are verified to exist, but not used. Accordingly, in anembodiment, the largest peaks {circle around (1)} and the second largestpeaks {circle around (2)} are compared in the following combinations:2-1-1; 1-2-1; 2-2-1.

Series III represents a third set of peak comparisons. In some cases,the largest peak from F_(b) or F_(am) or both may be the last peak,which is also often erroneous. As such, comparisons may be made usingthe second largest peaks {circle around (3)} from F_(b) and F_(am) andthe corresponding twins {circle around (6)}. Accordingly, in anembodiment, these peaks are compared in the following combinations:3-3-1; 6-3-1; 3-6-1; 6-6-1.

Series IV represents yet another set of peak comparisons. In some cases,the largest peak from F_(s) is erroneous. Hence, comparisons may be madeusing the largest peak from F_(s)′, designated {circle around (3)}, andthe largest peak and corresponding twin from F_(b) and F_(am),designated {circle around (1)} and {circle around (4)}, as noted above.Accordingly, in an embodiment, these peaks are compared with each otherin the following combinations: 4-4-3; 4-1-3; 1-4-3; 1-1-3. In otherembodiments, other combinations are possible, for example, the twins tothe second largest peaks from F_(b) and F_(am), which are designated{circle around (5)}, could be used in various combinations with otherdesignated peaks described above. If all combinations fail to yield athree-way match 1240, then that particular window is rejected 1280.

FIG. 13 illustrates decision logic 1300 for advantageously deriving arobust value for respiration rate based upon a two-way match of thespectrums F_(b), F_(am) and F_(s) (or F_(s)′) from each of the baseline300, AM 400 and shape 500, 501 processors (FIG. 10) plus an additionalcondition 1390. In an embodiment, decision logic 1300 is used in theevent a respiration rate RR 1004 (FIG. 10) cannot be derived from athree-way match of the spectrums F_(b), F_(am) and F_(s) (or F_(s)′), asdescribed with respect to FIG. 12, above.

As shown in FIG. 13, in a series V, the largest peaks from F_(b) andF_(s), denoted 1_1 (without utilizing F_(am)) are compared for a two-waymatch 1330. If there is a match, an additional condition 1370 must bemet. In an embodiment, the condition 1390 is that the matchingfrequencies of F_(b) and F_(s) must be within a predetermined differenceof the smoothed respiration rate (RRs) 1005 (FIG. 10). In an embodiment,the predetermined difference is 1 bpm. If so, the matching frequenciesare output as the respiration rate RR 1380. If not, the largest peaksfrom F_(am) and F_(s), denoted _11 (without utilizing F_(b)) are alsocompared for a match 1330. If there is a match from this comparison andthe additional condition 1370 is met, then the matching frequencies areoutput as the respiration rate RR 1380. If these combinations arecompared without a match 1350, then a series VI is utilized.

Also shown in FIG. 13, in a series VI, the various peaks from F_(b) andF_(am), each denote 1-6, are compared for a two-way match 1330. If thereis a match, the additional condition 1370 must be met. If allcombinations, e.g. 11, 12, 13 . . . 21, 22, 23 . . . 36, 46, 56 aretried without a match or there is a match but the additional conditionis not met, the window is rejected 1360. In other embodiments, otherpeaks are compared 1330 and other conditions 1390 must be met.

FIG. 14 illustrates a physiological monitoring system 1400 thatincorporates a plethysmographic respiration processor 100 (FIG. 1), asdescribed above. The monitoring system 1400 has a monitor 1410, anoptical sensor 1420 and an interconnect cable 1430 connecting themonitor 1410 and sensor 1420. The monitoring system 1400 generatesphysiological parameters that indicate one or more aspects of a person'sphysical condition, including, advantageously, a plethysmograph-derivedrespiration rate. The sensor 1420 attaches to a tissue site 10, such asa fingertip, and is capable of irradiating the tissue site 10 withdiffering wavelengths of light and detecting the light after attenuationby pulsatile blood flow within the tissue site 10. The monitor 1410communicates with the sensor 1420 via the interconnect cable 1430 toreceive one or more detected intensity signals and to derive from thoseintensity signals one or more physiological parameters. The monitor alsohas a display 1412 for presenting parameter values, includingrespiration rate (RR). Controls 1415 set alarm limits, processing modes,display formats and more. An audio transducer 1416 provides alarmsounds, pulse beeps and button press feedback to name a few. Indicators1418 show monitor status. The display 1412 may include readouts, coloredlights or graphics generated by LEDs, LCDs or CRTs to name a few and iscapable of displaying indicia representative of calculated physiologicalparameters, including respiration rate, and waveforms, includingplethysmographs. The display 1412 is also capable of showing historicalor trending data related to one or more of the measured parameters orcombinations of the measured parameters. User I/O may include, forexample, push buttons 1415 and indicators 1418. The push buttons may besoft keys with display-indicated functions or dedicated function keys1415. Other user I/O (not shown) may include keypads, touch screens,pointing devices, voice recognition devices and the like.

FIG. 15A illustrates a light absorption waveform 1501 at an illuminatedperipheral tissue site corresponding to a pulsatile blood volume at thatsite. The peripheral tissue site is illuminated by, and thecorresponding absorption is (indirectly) measured by, an optical sensor1420 (FIG. 14), as described above. A y-axis 1530 represents the totalamount of light absorbed by the tissue site, with time shown along anx-axis 1540. The total absorption is represented by layers, includingthe static absorption layers due to tissue 1532, venous blood 1534 and abaseline of arterial blood 1536. Also shown is a variable absorptionlayer 1538 due to the pulse-added volume of arterial blood that is usedto derive a plethysmograph, as described above and further with respectto FIG. 15B, below. This light absorption waveform 1501 varies as afunction of the wavelength of the optical sensor emitted light accordingto the blood constituency. Indeed, it is this wavelength variation thatallows a multi-parameter patient monitor to determine blood hemoglobincomponents and other blood constituents along with respiration ratecharacteristics, as described above.

As shown in FIG. 15A, a pulsatile blood volume 1538 is a function ofheart stroke volume, pressure gradient, arterial elasticity andperipheral resistance. The ideal pulsatile blood volume waveformdisplays a broad peripheral flow curve, with a short, steep inflow phase1516 followed by a 3 to 4 times longer outflow phase 1518. The inflowphase 1516 is the result of tissue distention by the rapid blood volumeinflow during ventricular systole. During the outflow phase 1518, bloodflow continues into the vascular bed during diastole. The end diastolicbaseline 1514 indicates the minimum basal tissue perfusion. During theoutflow phase 1518 is a dicrotic notch 1515. Classically, the dicroticnotch 1515 is attributed to closure of the aortic valve at the end ofventricular systole. However, it is also a function of reflection fromthe periphery of an initial, fast propagating pressure pulse that occursupon the opening of the aortic valve preceding the arterial flow wave.Pulsatile blood volume varies with physiological properties such asheart stroke, vessel size, elasticity and vascularization, to name afew. Accordingly, the blood flow waveform shape can vary significantlyfrom individual to individual and between tissue sites.

FIG. 15B illustrates a plethysmograph waveform 1502 detected by anoptical sensor 1420 (FIG. 14). In particular, detected intensity isshown along the y-axis 1550 versus time shown along the x-axis 1560. Theplethysmograph waveform 1502 is a time series of plethysmograph(“pleth”) pulses and relates to the time-varying pulsatile blood volume1538 (FIG. 15A) measured at a particular location on a person, referredto herein as a “tissue site.” A tissue site can be a fingertip, earlobe, toe, nose or forehead to name just as few. A person is used hereinas the referenced subject of optical sensor measurements, but otherliving species also have a measurable pleth and are included within thescope of this disclosure.

As shown in FIG. 15B, an optical sensor 1420 (FIG. 14) does not directlydetect absorption and, hence, does not directly measure the volumewaveform 1538 (FIG. 15A). However, the plethysmograph waveform 1502 ismerely an out-of-phase version of the volume profile 1538. Stateddifferently, the plethysmograph waveform 1502 varies inversely with thepulsatile blood volume 1538. In particular, the peak detected intensity1554 occurs at minimum volume 1514 and the minimum detected intensity1552 occurs at maximum volume 1512. Further, a rapid rise in volumeduring the inflow phase 1516 is reflected in a rapid decline inintensity 1556; and the gradual decline in volume during the outflowphase 1518 is reflected in a gradual increase 1558 in detectedintensity. The intensity waveform 1502 also displays a dicrotic notch1555.

FIG. 16 further illustrates a physiological monitoring system 1600having an optical sensor 1620 attached to a tissue site 10, a monitor1610 and an interconnecting sensor cable 1620. The sensor 1620 hasemitters 1622, each of which transmit light of a specified wavelength.Drivers 1654, 1655 convert digital control signals into analog drivesignals capable of activating the emitters 1622. A front-end 1652, 1653converts composite analog intensity signal(s) from the detector(s) 1624into digital data input to a digital signal processor (DSP) 1658. TheDSP 1658 may comprise any of a wide variety of data and/or signalprocessors capable of executing programs for determining physiologicalparameters from input data. In an embodiment, the DSP executes firmware1659 including pre-processors, respiration processors and postprocessors, such as described with respect to FIGS. 1-13, above.

Also shown in FIG. 16, an instrument manager 1682 may comprise one ormore microcontrollers controlling system management, such as monitoringthe activity of the DSP 1658. The instrument manager 1682 has aninterface port 1683 for monitor communications. In an embodiment, theinterface port 1683 has a display driver, an audio driver, user inputsand I/O for driving displays and alarms, responding to buttons andkeypads and providing external device input/output communications. In anembodiment, the displays can indicate a variety of physiologicalparameters 1686 such as respiration rate (RR), pulse rate (PR),plethysmograph (pleth), perfusion index (PI), pleth variability index(PVI), signal quality (IQ) and values for blood constituents includingoxygen saturation (SpO₂), carboxyhemoglobin (HbCO), methemoglobin(HbMet), total hemoglobin (Hbt) and oxygen content (0C) as well asinstrument and sensor status, such as sensor life, to name but a few.

FIG. 17 illustrates a demodulator 1700 having a modulated/multiplexeddetector signal 1703 input and demodulated signal 1705 outputs. That is,the demodulator input 1703 is the result of a detector 1624 (FIG. 16)response to N emitter wavelengths 1622 (FIG. 16) that are cyclicallyturned on and off by emitter drivers 1654 (FIG. 16) so as to illuminatea tissue site with multiple wavelength optical radiation, as is wellknown in the pulse oximetry art. The digitized detector signal 1703corresponds to the ND converter 1657 (FIG. 16) input to the DSP 1658(FIG. 16). The DSP has demodulator 1700 (preprocessor) firmware 1659which generates N channels of demodulated signals r₁(t), r₂(t), . . . ,r_(N)(t) 1705 in response. One signal r_(i)(t) corresponding to eachemitter wavelength 1622. These demodulated signals are plethysmographs,as described above.

The demodulator 1700 has mixers 1730 and low pass filters 1740 for eachchannel and demodulating signals d_(i)(t) 1704 provided to each mixer1730. The demodulating signals are linear combinations of (orthogonal)basis functions of the form

$\begin{matrix}{{d_{i}(t)} = {\sum\limits_{j = 1}^{M}{\beta_{i\; j} \cdot {\phi_{j}(t)}}}} & \left( {{EQ}.\mspace{14mu} 5} \right)\end{matrix}$which are derived by approximating the optical response of the emittersto on/off periods of the emitter drivers. M is the number of basisfunctions needed to approximate such optical responses. φ_(j)(t) is thej^(th) basis function used by the demodulator. In one embodiment, thebasis functions are of the form

$\begin{matrix}{{{\phi_{j}(t)} = {\sin\left( {{\frac{2\pi}{T}j\; t} + {b_{j}\frac{\pi}{2}}} \right)}};{b_{j} \in \left\lbrack {0,1} \right\rbrack}} & \left( {{EQ}.\mspace{14mu} 6} \right)\end{matrix}$where T is the period of the repeating on/off patterns of the emitterdrivers. Accordingly, the lowpass filter outputs 1705 are r₁(t), r₂(t),. . . , r_(N)(t), which are estimates of absorption for each emitterwavelength in view of noise n(t) that is additive to each channel.Plethysmograph demodulators are described in U.S. Pat. No. 5,919,134titled Method and Apparatus for Demodulating Signals in a Pulse OximetrySystem, issued Jul. 6, 1999; U.S. Pat. No. 7,003,338 titled Method andApparatus for Reducing Coupling Between Signals, issued Feb. 21, 2006;and U.S. patent application Ser. No. 13/037,321 titled PlethysmographFilter, filed Feb. 28, 2011; all assigned to Masimo Corporation andincorporated by reference herein.

Advantageously, a plethysmographic respiration processor 100 (FIG. 1) isimplemented on an advanced pulse oximetry monitor or an advanced bloodparameter monitor, as described above. Although a plethysmographicrespiration processor is described above with respect to derivingrespiration rate from a plethysmograph waveform, in other embodiments, aplethysmographic respiration processor may be used to derive otherrespiration-related parameters. In a particularly advantageousembodiment, a plethysmographic respiration processor is used inconjunction with an acoustic monitor or combined blood parameter andacoustic monitor so as to improve the accuracy of, robustness of, orotherwise supplement acoustic-derived respiration rate measurements orother acoustic-derived respiration parameters.

A plethysmographic respiration processor has been disclosed in detail inconnection with various embodiments. These embodiments are disclosed byway of examples only and are not to limit the scope of the claimsherein. One of ordinary skill in art will appreciate many variations andmodifications.

What is claimed is:
 1. A plethysmographic respiration detectorresponsive to respiratory effects appearing on a detected intensitywaveform measured with an optical sensor at a blood perfused peripheraltissue site so as to provide a measurement of respiration rate, theplethysmographic respiration detector comprising: a preprocessorconfigured to identify a portion of a plethysmograph waveform whichincludes a physiologically acceptable series of plethysmograph waveformpulses; one or more processors configured to use a plurality ofdifferent techniques to derive a plurality of different frequencyspectrums using the portion of the plethysmograph waveform, each of theplurality of different frequency spectrums responsive to at least onerespiratory effect on the portion of the plethysmograph waveform andincluding a plurality of local maximums, the one or more processorscomprising at least two of: a baseline processor configured to receivethe portion of the plethysmograph waveform and output a baselinefrequency spectrum responsive to a respiratory-induced baseline shift ofthe portion of the plethysmograph waveform, an amplitude modulation (AM)processor configured to receive the portion of the plethysmographwaveform and output an AM frequency spectrum responsive to arespiratory-induced amplitude modulation of the portion of theplethysmograph waveform, and a shape modulation (SM) processorconfigured to receive the portion of the plethysmograph waveform andoutput a SM frequency spectrum responsive to a respiratory-induced shapemodulation of the portion of the plethysmograph waveform, wherein afirst frequency spectrum of the plurality of different frequencyspectrums is one of the baseline frequency spectrum, the AM frequencyspectrum, and the SM frequency spectrum, and a second frequency spectrumof the plurality of different frequency spectrums is different from thefirst frequency spectrum and is one of the baseline frequency spectrum,the AM frequency spectrum, and the SM frequency spectrum; and a decisionlogic configured to determine a respiration rate based at least on acomparison of one or more local maximums of the first frequency spectrumand one or more local maximums of the second frequency spectrum, thedecision logic comprising: a peak detector configured to operate on atleast two of the baseline frequency spectrum, the AM frequency spectrum,and the SM frequency spectrum so as to determine the local maximums ofthe at least two of the baseline frequency spectrum, the AM frequencyspectrum, and the SM frequency spectrum, and a comparator configured todetermine if the local maximums of the at least two of the baselinefrequency spectrum, the AM frequency spectrum, and the SM frequencyspectrum occur at matching frequencies within a first tolerance, whereinthe decision logic is configured to generate a respiration rate outputif the comparator finds at least one local maximum of each of the atleast two of the baseline frequency spectrum, the AM frequency spectrum,and the SM frequency spectrum occur at matching frequencies within thefirst tolerance.
 2. The plethysmographic respiration detector accordingto claim 1 wherein the decision logic is configured to determine therespiration rate a plurality of times over portions of theplethysmograph waveform to generate a plurality of respiration rateoutputs, and further comprising a smoother configured to operate on theplurality of the respiration rate outputs so as to derive a smoothedrespiration rate output.
 3. The plethysmographic respiration detectoraccording to claim 2 wherein the decision logic is further configured toreject an additional respiration rate if it differs from the smoothedrespiration rate output by more than a first amount.
 4. Theplethysmographic respiration detector according to claim 1 wherein therespiration rate comprises a frequency of one of the local maximums ofthe first frequency spectrum.
 5. The plethysmographic respirationdetector according to claim 1 wherein the peak detector is configured toidentify frequencies of the local maximums of the first frequencyspectrum relative to a frequency corresponding to a pulse rate, and thecomparator is configured to determine if the local maximums of the firstfrequency spectrum and the second frequency spectrum occur at matchingfrequencies within the first tolerance by comparing individual localmaximums of the first frequency spectrum and individual local maximumsof the second frequency spectrum in an order based at least on theidentified frequencies of the local maximums of the first frequencyspectrum relative to the frequency corresponding to the pulse rate. 6.The plethysmographic respiration detector according to claim 1 whereinthe comparator is configured to determine if the local maximums of thefirst frequency spectrum and the second frequency spectrum occur atmatching frequencies within the first tolerance by comparing individuallocal maximums of the first frequency spectrum and individual localmaximums of the second frequency spectrum in an order based at least onmagnitudes of the local maximums of the first frequency spectrum.
 7. Theplethysmographic respiration detector according to claim 1 wherein thecomparator is configured to compare the local maximums of the firstfrequency spectrum and the second frequency spectrum until: (1) thecomparator determines that one local maximum of the first frequencyspectrum and one local maximum of the second frequency spectrum occur atmatching frequencies within the first tolerance; or (2) the comparatorcompares at least two of the local maximums of the first frequencyspectrum and at least two of the local maximums of the second frequencyspectrum and determines that none of the at least two of the localmaximums of the first frequency spectrum and none of the at least two ofthe local maximums of the second frequency spectrum occur at matchingfrequencies within the first tolerance.
 8. The plethysmographicrespiration detector according to claim 7 wherein the decision logic isconfigured to determine no respiration rate if the comparator comparesthe at least two of the local maximums of the first frequency spectrumand the at least two of the local maximums of the second frequencyspectrum and determines that none of the at least two of the localmaximums of the first frequency spectrum and none of the at least two ofthe local maximums of the second frequency spectrum occur at matchingfrequencies within the first tolerance.
 9. The plethysmographicrespiration detector according to claim 1 wherein the one or moreprocessors comprises the baseline processor, the baseline processorcomprising: a first signal conditioner configured to generate a firstconditioned pleth from the portion of the plethysmograph waveform; and afirst frequency transform configured to receive the first conditionedpleth and generate the baseline frequency spectrum, wherein the firstfrequency spectrum is the baseline frequency spectrum.
 10. Theplethysmographic respiration detector according to claim 1 wherein theone or more processors comprises the AM processor, the AM processorcomprising: a second signal conditioner configured to generate a secondconditioned pleth from the portion of the plethysmograph waveform; ademodulator configured to demodulate the second conditioned pleth togenerate a demodulated pleth; and a second frequency transformconfigured to receive the demodulated pleth and generate the AMfrequency spectrum, wherein the first frequency spectrum is the AMfrequency spectrum.
 11. The plethysmographic respiration detectoraccording to claim 1 wherein the one or more processors comprises the SMprocessor, the SM processor comprising: a third signal conditionerconfigured to generate a third conditioned pleth from the portion of theplethysmograph waveform; a feature extractor configured to generate amodulated metric from the third conditioned pleth; and a third frequencytransform configured to generate the SM frequency spectrum based atleast on the modulated metric, wherein the first frequency spectrum isthe SM frequency spectrum.
 12. A plethysmographic respiration detectorresponsive to respiratory effects appearing on a detected intensitywaveform measured with an optical sensor at a blood perfused peripheraltissue site so as to provide a measurement of respiration rate, theplethysmographic respiration detector comprising: a preprocessorconfigured to identify a portion of a plethysmograph waveform whichincludes a physiologically acceptable series of plethysmograph waveformpulses; one or more processors configured to use a plurality ofdifferent techniques to derive a plurality of different frequencyspectrums using the portion of the plethysmograph waveform, each of theplurality of different frequency spectrums responsive to at least onerespiratory effect on the portion of the plethysmograph waveform andincluding a plurality of local maximums, the one or more processorscomprising: a baseline processor configured to receive the portion ofthe plethysmograph waveform and output a baseline frequency spectrumresponsive to a respiratory-induced baseline shift of the portion of theplethysmograph waveform, an amplitude modulation (AM) processorconfigured to receive the portion of the plethysmograph waveform andoutput an AM frequency spectrum responsive to a respiratory-inducedamplitude modulation of the portion of the plethysmograph waveform, anda shape modulation (SM) processor configured to receive the portion ofthe plethysmograph waveform and output a SM frequency spectrumresponsive to a respiratory-induced shape modulation of the portion ofthe plethysmograph waveform, wherein a first frequency spectrum of theplurality of different frequency spectrums is one of the baselinefrequency spectrum, the AM frequency spectrum, and the SM frequencyspectrum, and a second frequency spectrum of the plurality of differentfrequency spectrums is different from the first frequency spectrum andis one of the baseline frequency spectrum, the AM frequency spectrum,and the SM frequency spectrum; and a decision logic configured todetermine a respiration rate based at least on a comparison of one ormore local maximums of the first frequency spectrum and one or morelocal maximums of the second frequency spectrum, the decision logiccomprising: a peak detector configured to operate on each of thebaseline frequency spectrum, the AM frequency spectrum, and the SMfrequency spectrum so as to determine the local maximums of the baselinefrequency spectrum, the AM frequency spectrum, and the SM frequencyspectrum, and a comparator configured to determine if the local maximumsof the baseline frequency spectrum, the AM frequency spectrum, and theSM frequency spectrum occur at matching frequencies within a firsttolerance, wherein the decision logic is configured to generate arespiration rate output if the comparator finds at least one localmaximum of each of the baseline frequency spectrum, the AM frequencyspectrum, and the SM frequency spectrum occur at matching frequencieswithin the first tolerance.
 13. The plethysmographic respirationdetector according to claim 12 wherein the decision logic is configuredto determine the respiration rate a plurality of times over portions ofthe plethysmograph waveform to generate a plurality of respiration rateoutputs, and further comprising a smoother configured to operate on theplurality of the respiration rate outputs so as to derive a smoothedrespiration rate output.
 14. The plethysmographic respiration detectoraccording to claim 12 wherein the decision logic is further configuredto reject an additional respiration rate if it differs from the smoothedrespiration rate output by more than a first amount.
 15. Theplethysmographic respiration detector according to claim 12 wherein therespiration rate comprises a frequency of one of the local maximums ofthe first frequency spectrum.
 16. The plethysmographic respirationdetector according to claim 12 wherein the baseline processor comprises:a first signal conditioner configured to generate a first conditionedpleth from the portion of the plethysmograph waveform; and a firstfrequency transform configured to receive the first conditioned plethand generate the baseline frequency spectrum, wherein the firstfrequency spectrum is the baseline frequency spectrum.
 17. Theplethysmographic respiration detector according to claim 12 wherein theAM processor comprises: a second signal conditioner configured togenerate a second conditioned pleth from the portion of theplethysmograph waveform; a demodulator configured to demodulate thesecond conditioned pleth to generate a demodulated pleth; and a secondfrequency transform configured to receive the demodulated pleth andgenerate the AM frequency spectrum, wherein the first frequency spectrumis the AM frequency spectrum.
 18. The plethysmographic respirationdetector according to claim 12 wherein the SM processor comprises: athird signal conditioner configured to generate a third conditionedpleth from the portion of the plethysmograph waveform; a featureextractor configured to generate a modulated metric from the thirdconditioned pleth; and a third frequency transform configured togenerate the SM frequency spectrum based at least on the modulatedmetric, wherein the first frequency spectrum is the SM frequencyspectrum.